Computational physics examples as IPython Notebooks.
Simulates the simple pendulum and damped simple pendulum
Computing the trajectory of a projectile moving through the air, subject to wind and air drag.
Discusses the chaotic motion of the double pendulum using a phase-space diagram
Analyzes data from the Cavendish experiment using curve fitting. The Gravitational Constant is estimated.
The motion of a rolling object on an arbitrary track is analyzed.
The 4th order Runge-Kutta method was used to integrate the equations of motion for the system, then the pendulum was stabilised on its inverted equilibrium point using a proportional gain controller and linear quadratic regulator.
Computes the precession of Mercury by linear extrapolation.
Applying the explicit and implicit Euler methods and the fourth order Runge-Kutta method to calculate the trajectory of the Earth around the Sun.
Studying how a third mass behaves in the effective gravitational potential resulting from two opposing masses (here: Sun and Earth).
Discussion of orbits in the Schwarzschild Geometry.
Applying the fourth order Runge-Kutta method and the adaptive step size Runge-Kutta method to calculate the trajectories of three bodies.
Explaining the concept and simulating gravitational slingshot of a spacecraft passing a planet.
Solving the Friedmann equations to model the expansion of our universe.
Plotting the paths of light rays travelling through a gradient-index optical fiber.
Filters and analyzes images using Fourier transforms.
Using Fourier transforms to filter and analyzing hybrid images.
Computing the internal energy, specific heat and magnetisation in the 1D and 2D Ising model. An analytical solution to the XY model is also provided.
A brief introduction to Brownian motion and its connection with diffusion. A system of Brownian particles in 2D is simulated and visualised.
Using the Metropolis algorithm to approximate the magnetization and specific heat for a 2D Ising lattice.
Using the method of forward shooting to determine numerically the eigenenergies of the quantum harmonic oscillator in one dimension.
Using Newton's method to calculate the band structure for the simple Dirac comb potential in one dimension.
Computing the size of the hydrogen atom using Monte Carlo integration.
Calculating the eigenenergies of the lowest states for a one-dimensional double-well potential.
Employing Monte Carlo integration to determine the "shape" of the hydrogen molecule ion.
Using a forward-shooting method to determine the eigenenergies and eigenfunctions of an asymmetric potential in one dimension.
The eigenenergies of a system are found by discretizing the Schrödinger equation and finding the eigenvalues of the resulting matrix.
A one-dimensional wave-packet is propagated forward in time for various different potentials.
The Time-Dependent Schrödinger equation is solved by expressing the solution as a linear combination of (stationary) solutions of the Time-Independent Schrödinger equation.
Solving the time independent Schrödinger equation in one dimension using matrix diagonalisation for five different potentials.
Calculates and plots the electric fields and potentials around an arbitrary number of point charges.
Using numerical tools to describe and analyse a quadrupole mass spectrometer.
Calculating the total current propagating through a resistor network.
Solving Lorentz force law for a charged particle traveling in a uniform magnetic field using Euler's method.
A simple physical model that approximates the interaction between a pair of neutral atoms or molecules.
Self-avoiding random walks on the square lattice are performed using random sampling. The probability distribution of how many steps a random walker uses before it traps itself is studied. The notebook is based on an article by S. and P. C. Hemmer.
Describing aggregation using random walk and estimating its fractal dimension
Calculating the speed of a passing train by Fourier analysis of the corresponding sound file.
Analyzing sloshing using a numerical approach based on a linear model, which reduces the problem to a Steklov eigenvalue problem.
Programming with sounds and using Fourier transforms to filter sound signals.